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Abstract

In three-dimensional microscopy, the image formation process is inherently depth variant (DV) due to the refractive
index mismatch between the imaging layers. In this study, we present a quantitative comparison among different image
restoration techniques developed based on a depth-variant (DV) imaging model for fluorescence microscopy. The
imaging models employed by these methods approximate DV imaging by either stratifying the object space (analogous
to the discrete Fourier transform (DFT) “overlap-add” method) or image space (analogous to the DFT “overlap-save”
method). We compare DV implementations based on maximum likelihood (ML) estimation and a previously developed
expectation maximization algorithm to a ML conjugate gradient algorithm, using both these stratification approaches in
order to assess their impact on the restoration methods. Simulations show that better restoration results are achieved
with iterative methods implemented using the overlap-add method than with their implementation using the overlap-save
method. However, the overlap-save method makes it possible to implement a non-iterative DV inverse filter that can
trade off accuracy of the achieved result for computational speed. Results from a non-iterative regularized inverse
filtering approach are also presented.

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Journal of Applied Remote SensingJournal of Astronomical Telescopes Instruments and SystemsJournal of Biomedical OpticsJournal of Electronic ImagingJournal of Medical ImagingJournal of Micro/Nanolithography, MEMS, and MOEMSJournal of NanophotonicsJournal of Photonics for EnergyNeurophotonicsOptical EngineeringSPIE Reviews